Adaptation Actions for a Changing Arctic: Perspectives from the Barents Area

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Adaptation Actions for a Changing Arctic: Perspectives from the Barents Area

comparable, scalable data in the circumpolar north. This highlights the value of complementary forms of knowledge to provide needed insights. It is important to note that knowledge is not neutral; relevant knowledge helps to shape perception of the action options in any given situation. For example, knowledge of the status of ecosystem capacity to provide desired functions – and the trajectory of that capacity vis-a-vis anticipated changes – can guide decisions about what actions to take or not take. Similarly, knowledge of functional diversity and of response diversity is important in considering how to strengthen resilience in a given social-ecological system, as is knowledge of social and cultural needs and trends. Even in conventional science this is an inherently social, even political, process in which proponents for competing views may struggle for primacy (Kuhn, 1970). Power can therefore influence competition regarding what kinds of knowledge are considered relevant or legitimate. The very ways in which social problems are defined tend to identify particular types of knowledge and expertise as relevant and appropriate (Carson, 2008). Definitions of what constitutes legitimate knowledge also vary depending widely on disciplinary training and on methods of data collection and analysis, or on whether it is considered scientific (Jasanoff and Martello, 2004). In this respect, knowledge is not a neutral asset, but subject to claims regarding knowledge domains and reflections on whose knowledge counts. The important precursor for knowledge is the capacity for ongoing learning and thereby adding to existing knowledge,ormodifying or replacing knowledge that proves incomplete or flawed. Integrating different forms of knowledge and understanding changing conditions both entail learning.Learning takes place via different kinds of process, some of which are better suited than others to the types of challenge communities face in preparing for an uncertain future. Table 8.1 highlights these different modes, which vary according to several factors, including assumptions about the nature of the learning and decision environment,who the decision-makers are, and how systematic is the process of learning and incorporating new knowledge. It should be emphasized here that the category ‘deliberation with analysis’most closely fits the type of learning process needed for navigating under conditions of uncertainty and was developed with the aim of providing knowledge for decision support in the context of climate change.With its assumptions of changing conditions,focus on learning as an iterative process with attention to ongoing monitoring and incorporation of new learning, and collaborative modes of learning, it meshes well with the criteria outlined for the development of resilience indicators proposed in this chapter (Section 8.5). 8.3.4 Self-organization Filotas et al. (2014) defined self-organization as “ the process whereby local interaction among a system’s components cause coherent patterns, entities, or behaviors to emerge at higher scales of the hierarchy, which in turn affect the original components

Knowledge is here defined in a broad sense, with experiential and experimental knowledge taken as largely complementary and therefore parts of a whole (Watson et al., 2003; Folke, 2004, 2006; Folke et al., 2004). The broad consensus regarding the importance of knowledge of the Arctic is clearly apparent in the investment of time and other resources in the scientific endeavors of theArctic Council working groups, and in the efforts of the Arctic Council to determine how to better integrate other forms of knowledge, such as traditional knowledge 18 and local knowledge. These efforts are wide-ranging, with the common thread being the desire to better understand how human activities affect people and nature in the Arctic, and how such effects might in turn impact future Arctic development. Experiential knowledge, of which traditional and local knowledge are particular types, is distinguished from conventional scientific knowledge. Where conventional science produces knowledge on the basis of focused experimental studies and simulations that draw on bodies of accumulated research, use of experiential knowledge draws on and builds local-scale expertise and capacity. It can provide baseline data and a source of historical impacts and adaptations in Arctic communities that can also help guide and formulate hypotheses for conventional research (Riedlinger and Berkes, 2001). These complementary modes of knowledge are important in filling gaps, informing one another, addressing change driven by developments at different scales, and changes that reach beyond previous experience. In short, diversity in knowledge is strengthened when conventional science is combined with traditional and local (experiential, observational) knowledge. The fundamental challenge with integrating diverse knowledge forms, including traditional knowledge, is that they are often incommensurable – not directly comparable or translatable (see, for example,Thomas Kuhn’s classic work on scientific revolution; Kuhn, 1970). The challenge entailed in comparing or bridging knowledge systems is on display in the debate between Howard and Widdowson (1996) and Berkes and Henley (1997) in the Canadian journal Policy Options , where Berkes and Henley argued that shared learning and collaborative-production of knowledge are essential processes for integrating different knowledge traditions. In such processes, new knowledge is created that is more than the sum of its parts (Jasanoff, 2004). Knowledge diversity is also important in other ways. For example, in their work on Arctic Social Indicators (ASI), Nymand Larsen et al. (2010) concluded that the number of years of formal education completed is the preferred proxy indicator of knowledge resources because the data are readily available.However,meaningful and relevant knowledge such as traditional or local knowledge is missed because one criterion of the ASI work is that the data be readily available, that is, collected on an ongoing basis. This is not a flaw of the highly thoughtful work carried out by research involved with the ASI, but a consequence of the challenges entailed in collecting

18 There is ongoing discussion as to whether the label ’traditional knowledge’ adequately represents the nature of the knowledge held by the indigenous peoples of the Arctic, the ways in which it is systematically collected and passed on, and who is qualified to assess its value. See Johnson et al. (2016) for a summary.